Key result
A predictive model using respiratory sinus arrhythmia, heart rate, and disease duration predicted symptomatic neuropathy in 80% of high-risk vs 0% of low-risk patients over 2 years (P<0.05).
Why the study?
Can a model incorporating heart rate, disease duration, and respiratory sinus arrhythmia predict the development of symptomatic neuropathy in diabetic patients?
Population
67 diabetic patients, including 32 with symptomatic neuropathy and 35 asymptomatic.
Design
Cohort, Follow-up interviewer was unaware of the results of the modeling.
Follow-up
2 years (for a subset of 9 asymptomatic patients)
Authors
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May support neuropathy risk stratification in diabetes; hypothesis-generating from Level 3 data and needs prospective validation.
Observational (n=67)
Can a model incorporating heart rate, disease duration, and respiratory sinus arrhythmia predict the development of symptomatic neuropathy in diabetic patients?
Absolute Event Rate: 80% vs 0%
p-value: p=<0.05
A predictive model incorporating respiratory sinus arrhythmia, heart rate, and disease duration can identify diabetic patients at high risk of developing symptomatic neuropathy.
Weinberg et al. (1986) conducted an observational in Diabetes (n=67). High probability of symptoms assigned by predictive model (diminished respiratory sinus arrhythmia, heart rate, disease duration) vs. Low probability assigned by predictive model was evaluated on Development of symptomatic neuropathy (SN) (p=<0.05). A predictive model using respiratory sinus arrhythmia, heart rate, and disease duration predicted symptomatic neuropathy in 80% of high-risk vs 0% of low-risk patients over 2 years (P<0.05).